Furthermore, McMahan et al. [1]} point to an inherent problem of the federated learning approach, when it is applied on non-Independent, Identically Distributed (non-IID) client data. Since on-device training data is naturally grouped by speaker, there are different statistical properties in every client, which may degrade the model performance. To counter this problem, a cross-silo training approach can be beneficial and will be subject of our future investigations. Federated Variational Noise (FVN) will also be investigated, which has shown its value in recovering FL-ASR performance despite non-IID client data [2]}.
